Method for Identifying Verifiable Statements in Text

ABSTRACT

A method, system and computer-usable medium are disclosed for identifying verifiable statements in a corpus of text. A training corpus of text containing manually annotated instances of verifiable and non-verifiable statements is processed to parse the text into segmented statements, which are in turn processed to extract features. The extracted features and the annotated statements are then processed with a machine learning algorithm to generate a verifiable statement classification model. In turn, the verifiable statement classification model is referenced by a verifiable statement classification system to distinguish verifiable and non-verifiable statements contained within an input corpus of text.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates in general to the field of computers andsimilar technologies, and in particular to software utilized in thisfield. Still more particularly, it relates to a method, system andcomputer-usable medium for identifying verifiable statements in a corpusof text.

2. Description of the Related Art

Factual utterances assert something that may be true or false. Incontrast, other utterances may have some other pragmatic role, such asopinions, declarations, exhortations, rhetorical exaggerations, and soforth. A subset of factual utterances is generally verifiable. Forexample, they may assert something that can be checked and potentiallyverified or falsified using authoritative sources. However, there may bemany reasons why a factual utterance may not be verifiable. As anexample, the factual utterance may be about the future. As anotherexample, the factual utterance may involve information that would not bereported in an authoritative source.

Ingesting a document, such as a transcript of a speech or debate, andingesting the verifiable statements it contains can prove challenging.Regardless, doing so is a common prerequisite for performing morecomplex Natural Language Processing (NLP) processing tasks, such as factchecking, search, summarization and so forth. For example, the followingparagraph was included in President Obama's 2013 State of the Unionspeech:

“Our first priority is making America a magnet for new jobs andmanufacturing. After shedding jobs for more than ten years, ourmanufacturers have added about 500,000 jobs over the last three.Caterpillar is bringing jobs back from Japan. Ford is bringing jobs backfrom Mexico. And this year, Apple will start making Macs in Americaagain.”

Within this paragraph, there are five claims that can be verified:

-   -   the US economy shed manufacturing jobs for more than 10 years    -   US manufacturers have added about 500,000 jobs over the past        three years    -   Caterpillar is moving manufacturing jobs from Japan to the US    -   Ford is moving manufacturing jobs from Mexico to the US    -   Apple will make more Macs in the US in 2012 than they did in        2011

Likewise, the initial statement of “making America a magnet for new jobsand manufacturing” is a declaration. However, existing known approachesare unable to reliably perform these sorts of classifications.Furthermore, while approaches are likewise known for classifyingsubjective statements within the body of a text, their use inclassifying objective statements is typically problematic andunreliable.

SUMMARY OF THE INVENTION

A method, system and computer-usable medium are disclosed foridentifying verifiable statements in a corpus of text. In variousembodiments, a training corpus of text containing manually annotatedinstances of verifiable and non-verifiable statements is processed toparse the text into segmented statements. The resulting segmentedstatements are then processed to extract features. In variousembodiments, the extracted features may correspond to sentiment, verbs,verb tense, nouns, proper nouns, magnitude, velocity, importance,quantified items, quantitative comparison operators, or reference.

The extracted features and the annotated statements are then processedwith a machine learning algorithm to generate a verifiable statementclassification model. In turn, the verifiable statement classificationmodel is referenced by a verifiable statement classification system todistinguish verifiable and non-verifiable statements contained within aninput corpus of text. The statements that have been identified asverifiable are then provided to a downstream process or verificationsystem for verification.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features and advantages made apparent to those skilled in theart by referencing the accompanying drawings. The use of the samereference number throughout the several figures designates a like orsimilar element.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion prioritization system and question/answer (QA) system connectedto a computer network;

FIG. 2 is a simplified block diagram of an information handling systemcapable of performing computing operations;

FIG. 3 is a simplified block diagram of a verifiable statementclassification system;

FIG. 4 is a generalized flowchart of the performance of operations togenerate a verifiable statement classification model; and

FIG. 5 is a generalized flowchart of the performance of operations toidentify verifiable statements in a corpus of text.

DETAILED DESCRIPTION

A method, system and computer-usable medium are disclosed foridentifying verifiable statements in a corpus of text. The presentinvention may be a system, a method, and/or a computer program product.In addition, selected aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and/or hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form ofcomputer program product embodied in a computer readable storage medium(or media) having computer readable program instructions thereon forcausing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a dynamic or static random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), a magnetic storage device, a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server or cluster of servers. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion prioritization system 10 and question/answer (QA) system 100connected to a computer network 140. The QA system 100 includes aknowledge manager 104 that is connected to a knowledge base 106 andconfigured to provide question/answer (QA) generation functionality forone or more content users who submit across the network 140 to the QAsystem 100. To assist with efficient sorting and presentation ofquestions to the QA system 100, the prioritization system 10 may beconnected to the computer network 140 to receive user questions, and mayinclude a plurality of subsystems which interact with cognitive systems,like the knowledge manager 100, to prioritize questions or requestsbeing submitted to the knowledge manager 100.

The Named Entity subsystem 12 receives and processes each question 11 byusing natural language (NL) processing to analyze each question andextract question topic information contained in the question, such asnamed entities, phrases, urgent terms, and/or other specified termswhich are stored in one or more domain entity dictionaries 13. Byleveraging a plurality of pluggable domain dictionaries relating todifferent domains or areas (e.g., travel, healthcare, electronics, gameshows, financial services), the domain dictionary 11 enables criticaland urgent words (e.g., “threat level”) from different domains (e.g.,“travel”) to be identified in each question based on their presence inthe domain dictionary 11. To this end, the Named Entity subsystem 12 mayuse a Natural Language Processing (NLP) routine to identify the questiontopic information in each question. As used herein, “NLP” refers to thefield of computer science, artificial intelligence, and linguisticsconcerned with the interactions between computers and human (natural)languages. In this context, NLP is related to the area of human-computerinteraction and natural language understanding by computer systems thatenable computer systems to derive meaning from human or natural languageinput. For example, NLP can be used to derive meaning from ahuman-oriented question such as, “What is the tallest mountain in NorthAmerica?” and to identify specified terms, such as named entities,phrases, or urgent terms contained in the question. The processidentifies key terms and attributes in the question and compares theidentified terms to the stored terms in the domain dictionary 13.

The Question Priority Manager subsystem 14 performs additionalprocessing on each question to extract question context information ISA.In addition or in the alternative, the Question Priority Managersubsystem 14 may also extract server performance information 15B for thequestion prioritization system 10 and/or QA system 100. In selectedembodiments, the extracted question context information 15A may includedata that identifies the user context and location when the question wassubmitted or received. For example, the extracted question contextinformation 15A may include data that identifies the user who submittedthe question (e.g., through login credentials), the device or computerwhich sent the question, the channel over which the question wassubmitted, the location of the user or device that sent the question,any special interest location indicator (e.g., hospital, public-safetyanswering point, etc.), or other context-related data for the question.The Question Priority Manager subsystem 14 may also determine or extractselected server performance data 15B for the processing of eachquestion. In selected embodiments, the server performance information15B may include operational metric data relating to the availableprocessing resources at the question prioritization system 10 and/or QAsystem 100, such as operational or run-time data, CPU utilization data,available disk space data, bandwidth utilization data, etc. As part ofthe extracted information 15A/B, the Question Priority Manager subsystem14 may identify the SLA or QoS processing requirements that apply to thequestion being analyzed, the history of analysis and feedback for thequestion or submitting user, and the like. Using the question topicinformation and extracted question context and/or server performanceinformation, the Question Priority Manager subsystem 14 is configured topopulate feature values for the Priority Assignment Model 16 whichprovides a machine learning predictive model for generating a targetpriority values for the question, such as by using an artificialintelligence (AI) rule-based logic to determine and assign a questionurgency value to each question for purposes of prioritizing the responseprocessing of each question by the QA system 100.

The Prioritization Manager subsystem 17 performs additional sort or rankprocessing to organize the received questions based on at least theassociated target priority values such that high priority questions areput to the front of a prioritized question queue 18 for output asprioritized questions 19. In the question queue 18 of the PrioritizationManager subsystem 17, the highest priority question is placed at thefront for delivery to the assigned QA system 100. In selectedembodiments, the prioritized questions 19 from the PrioritizationManager subsystem 17 that have a specified target priority value may beassigned to a specific pipeline (e.g., QA System 100A) in the QA systemcluster 100. As will be appreciated, the Prioritization Managersubsystem 17 may use the question queue 18 as a message queue to providean asynchronous communications protocol for delivering prioritizedquestions 19 to the QA system 100 such that the Prioritization Managersubsystem 17 and QA system 100 do not need to interact with a questionqueue 18 at the same time by storing prioritized questions in thequestion queue 18 until the QA system 100 retrieves them. In this way, awider asynchronous network supports the passing of prioritized questionsas messages between different computer systems 100A, 100B, connectingmultiple applications and multiple operating systems. Messages can alsobe passed from queue to queue in order for a message to reach theultimate desired recipient. An example of a commercial implementation ofsuch messaging software is IBM's WebSphere MQ (previously MQ Series). Inselected embodiments, the organizational function of the PrioritizationManager subsystem 17 may be configured to convert over-subscribingquestions into asynchronous responses, even if they were asked in asynchronized fashion.

The QA system 100 may include one or more QA system pipelines 100A,100B, each of which includes a computing device 104 (comprising one ormore processors and one or more memories, and potentially any othercomputing device elements generally known in the art including buses,storage devices, communication interfaces, and the like) for processingquestions received over the network 140 from one or more users atcomputing devices (e.g., 110, 120, 130) connected over the network 140for communication with each other and with other devices or componentsvia one or more wired and/or wireless data communication links, whereeach communication link may comprise one or more of wires, routers,switches, transmitters, receivers, or the like. In this networkedarrangement, the QA system 100 and network 140 may enablequestion/answer (QA) generation functionality for one or more contentusers. Other embodiments of QA system 100 may be used with components,systems, sub-systems, and/or devices other than those that are depictedherein.

In each QA system pipeline 100A, 100B, a prioritized question 19 isreceived and prioritized for processing to generate an answer 20. Insequence, prioritized questions 19 are dequeued from the shared questionqueue 18, from which they are dequeued by the pipeline instances forprocessing in priority order rather than insertion order. In selectedembodiments, the question queue 18 may be implemented based on a“priority heap” data structure. During processing within a QA systempipeline (e.g., 100A), questions may be split into many subtasks whichrun concurrently. A single pipeline instance can process a number ofquestions concurrently, but only a certain number of subtasks. Inaddition, each QA system pipeline may include a prioritized queue (notshown) to manage the processing order of these subtasks, with thetop-level priority corresponding to the time that the correspondingquestion started (earliest has highest priority). However, it will beappreciated that such internal prioritization within each QA systempipeline may be augmented by the external target priority valuesgenerated for each question by the Question Priority Manager subsystem14 to take precedence or ranking priority over the question start time.In this way, more important or higher priority questions can “fasttrack” through the QA system pipeline if it is busy with already-runningquestions.

In the QA system 100, the knowledge manager 104 may be configured toreceive inputs from various sources. For example, knowledge manager 104may receive input from the question prioritization system 10, network140, a knowledge base or corpus of electronic documents 106 or otherdata, a content creator 108, content users, and other possible sourcesof input. In selected embodiments, some or all of the inputs toknowledge manager 104 may be routed through the network 140 and/or thequestion prioritization system 10. The various computing devices (e.g.,110, 120, 130, 150, 160, 170) on the network 140 may include accesspoints for content creators and content users. Some of the computingdevices may include devices for a database storing the corpus of data asthe body of information used by the knowledge manager 104 to generateanswers to cases. The network 140 may include local network connectionsand remote connections in various embodiments, such that knowledgemanager 104 may operate in environments of any size, including local andglobal, e.g., the Internet. Additionally, knowledge manager 104 servesas a front-end system that can make available a variety of knowledgeextracted from or represented in documents, network-accessible sourcesand/or structured data sources. In this manner, some processes populatethe knowledge manager with the knowledge manager also including inputinterfaces to receive knowledge requests and respond accordingly.

In one embodiment, the content creator creates content in a document 106for use as part of a corpus of data with knowledge manager 104. Thedocument 106 may include any file, text, article, or source of data(e.g., scholarly articles, dictionary definitions, encyclopediareferences, and the like) for use in knowledge manager 104. Contentusers may access knowledge manager 104 via a network connection or anInternet connection to the network 140, and may input questions toknowledge manager 104 that may be answered by the content in the corpusof data. As further described below, when a process evaluates a givensection of a document for semantic content, the process can use avariety of conventions to query it from the knowledge manager. Oneconvention is to send a well-formed question. Semantic content iscontent based on the relation between signifiers, such as words,phrases, signs, and symbols, and what they stand for, their denotation,or connotation. In other words, semantic content is content thatinterprets an expression, such as by using Natural Language (NL)Processing. In one embodiment, the process sends well-formed questions(e.g., natural language questions, etc.) to the knowledge manager.Knowledge manager 104 may interpret the question and provide a responseto the content user containing one or more answers to the question. Insome embodiments, knowledge manager 104 may provide a response to usersin a ranked list of answers.

In some illustrative embodiments, QA system 100 may be the IBM Watson™QA system available from International Business Machines Corporation ofArmonk, N.Y., which is augmented with the mechanisms of the illustrativeembodiments described hereafter. The IBM Watson™ knowledge managersystem may receive an input question which it then parses to extract themajor features of the question, that in turn are then used to formulatequeries that are applied to the corpus of data. Based on the applicationof the queries to the corpus of data, a set of hypotheses, or candidateanswers to the input question, are generated by looking across thecorpus of data for portions of the corpus of data that have somepotential for containing a valuable response to the input question.

The IBM Watson™ QA system then performs deep analysis on the language ofthe input prioritized question 19 and the language used in each of theportions of the corpus of data found during the application of thequeries using a variety of reasoning algorithms. There may be hundredsor even thousands of reasoning algorithms applied, each of whichperforms different analysis, e.g., comparisons, and generates a score.For example, some reasoning algorithms may look at the matching of termsand synonyms within the language of the input question and the foundportions of the corpus of data. Other reasoning algorithms may look attemporal or spatial features in the language, while others may evaluatethe source of the portion of the corpus of data and evaluate itsveracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the IBM Watson™ QA system. Thestatistical model may then be used to summarize a level of confidencethat the IBM Watson™ QA system has regarding the evidence that thepotential response, i.e. candidate answer, is inferred by the question.This process may be repeated for each of the candidate answers until theIBM Watson™ QA system identifies candidate answers that surface as beingsignificantly stronger than others and thus, generates a final answer,or ranked set of answers, for the input question. The QA system 100 thengenerates an output response or answer 20 with the final answer andassociated confidence and supporting evidence. More information aboutthe IBM Watson™ QA system may be obtained, for example, from the IBMCorporation website, IBM Redbooks, and the like. For example,information about the IBM Watson™ QA system can be found in Yuan et al.,“Watson and Healthcare,” IBM developerWorks, 2011 and “The Era ofCognitive Systems: An Inside Look at IBM Watson and How it Works” by RobHigh, IBM Redbooks, 2012.

Types of information handling systems that can utilize QA system 100range from small handheld devices, such as handheld computer/mobiletelephone 110 to large mainframe systems, such as mainframe computer170. Examples of handheld computer 110 include personal digitalassistants (PDAs), personal entertainment devices, such as MP3 players,portable televisions, and compact disc players. Other examples ofinformation handling systems include pen, or tablet, computer 120,laptop, or notebook, computer 130, personal computer system 150, andserver 160. As shown, the various information handling systems can benetworked together using computer network 140. Types of computer network140 that can be used to interconnect the various information handlingsystems include Local Area Networks (LANs), Wireless Local Area Networks(WLANs), the Internet, the Public Switched Telephone Network (PSTN),other wireless networks, and any other network topology that can be usedto interconnect the information handling systems. Many of theinformation handling systems include nonvolatile data stores, such ashard drives and/or nonvolatile memory. Some of the information handlingsystems may use separate nonvolatile data stores (e.g., server 160utilizes nonvolatile data store 165, and mainframe computer 170 utilizesnonvolatile data store 175). The nonvolatile data store can be acomponent that is external to the various information handling systemsor can be internal to one of the information handling systems. Anillustrative example of an information handling system showing anexemplary processor and various components commonly accessed by theprocessor is shown in FIG. 2.

FIG. 2 illustrates information handling system 200, more particularly, aprocessor and common components, which is a simplified example of acomputer system capable of performing the computing operations describedherein. Information handling system 200 includes one or more processors210 coupled to processor interface bus 212. Processor interface bus 212connects processors 210 to Northbridge 215, which is also known as theMemory Controller Hub (MCH). Northbridge 215 connects to system memory220 and provides a means for processor(s) 210 to access the systemmemory. Graphics controller 225 also connects to Northbridge 215. In oneembodiment, PCI Express bus 218 connects Northbridge 215 to graphicscontroller 225. Graphics controller 225 connects to display device 230,such as a computer monitor.

Northbridge 215 and Southbridge 235 connect to each other using bus 219.In one embodiment, the bus is a Direct Media Interface (DMI) bus thattransfers data at high speeds in each direction between Northbridge 215and Southbridge 235. In another embodiment, a Peripheral ComponentInterconnect (PCI) bus connects the Northbridge and the Southbridge.Southbridge 235, also known as the I/O Controller Hub (ICH) is a chipthat generally implements capabilities that operate at slower speedsthan the capabilities provided by the Northbridge. Southbridge 235typically provides various busses used to connect various components.These busses include, for example, PCI and PCI Express busses, an ISAbus, a System Management Bus (SMBus or SMB), and/or a Low Pin Count(LPC) bus. The LPC bus often connects low-bandwidth devices, such asboot ROM 296 and “legacy” I/O devices (using a “super I/O” chip). The“legacy” I/O devices (298) can include, for example, serial and parallelports, keyboard, mouse, and/or a floppy disk controller. Othercomponents often included in Southbridge 235 include a Direct MemoryAccess (DMA) controller, a Programmable Interrupt Controller (PIC), anda storage device controller, which connects Southbridge 235 tononvolatile storage device 285, such as a hard disk drive, using bus284.

ExpressCard 255 is a slot that connects hot-pluggable devices to theinformation handling system. ExpressCard 255 supports both PCI Expressand USB connectivity as it connects to Southbridge 235 using both theUniversal Serial Bus (USB) the PCI Express bus. Southbridge 235 includesUSB Controller 240 that provides USB connectivity to devices thatconnect to the USB. These devices include webcam (camera) 250, infrared(IR) receiver 248, keyboard and trackpad 244, and Bluetooth device 246,which provides for wireless personal area networks (PANs). USBController 240 also provides USB connectivity to other miscellaneous USBconnected devices 242, such as a mouse, removable nonvolatile storagedevice 245, modems, network cards, ISDN connectors, fax, printers, USBhubs, and many other types of USB connected devices. While removablenonvolatile storage device 245 is shown as a USB-connected device.removable nonvolatile storage device 245 could be connected using adifferent interface, such as a Firewire interface, etc.

Wireless Local Area Network (LAN) device 275 connects to Southbridge 235via the PCI or PCI Express bus 272. LAN device 275 typically implementsone of the IEEE 802.11 standards for over-the-air modulation techniquesto wireless communicate between information handling system 200 andanother computer system or device. Extensible Firmware Interface (EFI)manager 280 connects to Southbridge 235 via Serial Peripheral Interface(SPI) bus 278 and is used to interface between an operating system andplatform firmware. Optical storage device 290 connects to Southbridge235 using Serial ATA (SATA) bus 288. Serial ATA adapters and devicescommunicate over a high-speed serial link. The Serial ATA bus alsoconnects Southbridge 235 to other forms of storage devices, such as harddisk drives. Audio circuitry 260, such as a sound card, connects toSouthbridge 235 via bus 258. Audio circuitry 260 also providesfunctionality such as audio line-in and optical digital audio in port262, optical digital output and headphone jack 264, internal speakers266, and internal microphone 268. Ethernet controller 270 connects toSouthbridge 235 using a bus, such as the PCI or PCI Express bus.Ethernet controller 270 connects information handling system 200 to acomputer network, such as a Local Area Network (LAN), the Internet, andother public and private computer networks.

While FIG. 2 shows one information handling system, an informationhandling system may take many forms, some of which are shown in FIG. 1.For example, an information handling system may take the form of adesktop, server, portable, laptop, notebook, or other form factorcomputer or data processing system. In addition, an information handlingsystem may take other form factors such as a personal digital assistant(PDA), a gaming device, ATM machine, a portable telephone device, acommunication device or other devices that include a processor andmemory.

FIG. 3 is a simplified block diagram of a verifiable statementclassification system implemented in accordance with an embodiment ofthe invention. In various embodiments, a training corpus of text 302containing manually annotated instances of verifiable and non-verifiablestatements is processed to extract features 312. The extracted features312 and the annotated statements 314 are then processed with a machinelearning algorithm 316 to generate a verifiable statement classificationmodel 318. In turn, the verifiable statement classification model 318 isreferenced by a verifiable statement classification system 300 todistinguish verifiable and non-verifiable statements contained within aninput corpus of text 322.

In various embodiments, the verifiable statement classification system300 is implemented to distinguish between assertions within an inputcorpus of text 322 that state a claim that is appropriate forverification, as opposed to assertions that are either subjective innature, related to private information states that cannot be verified,or are not conducive to checking. In these and other embodiments, theverifiable statement classification system 300 is not implemented to tagall objective statements contained in the input corpus of text 322.Instead, the input corpus of text 322 is processed to by the verifiablestatement classification system 300 to automate the identification andclassification of statements that contain objective claims that areamenable to external verification.

Skilled practitioners of the art will be aware that the field ofsentiment analysis addresses a similar challenge, which is firstidentifying opinion statements, followed by then characterizing thetopic and opinion expressed about that topic. Those of skill in the artwill likewise be aware that opinion statements are not generallyverifiable. However, the task of identifying verifiable statements isnot as simple as identifying all statements that are not opinionstatements as there are many utterances that are neither opinions norverifiable, such as factual statements about the future. Other examplesinclude factual statements about private information, declarations,exhortations, rhetorical exaggerations, and so forth.

It will likewise be appreciated that detecting deceptive statement isdistinct from detecting whether a statement is verifiable. For example,a viewer may claim that a movie is really great when in fact theyactually disliked it. In this example, the viewer is being deceptive butis not making a verifiable statement. Furthermore, known approaches suchas pycho-linguistic observations about the viewer are more suited todetermining whether an individual is being deceptive, not to determiningwhether an individual statement made by the individual is factual.

Referring now to FIG. 3, statements within a candidate training corpusof text 302 is manually annotated as verifiable or not verifiable togenerate a training corpus of text 304. In various embodiments, thetraining corpus of text 304 is a preexisting corpus of text containingannotated statements that have been verified by a statement verificationservice, such as FactCheck.org. As an example, the training corpus oftext 304 may include a series of statements, such as this paragraph inPresident Obama's 2014 State of the Union Speech: “Here are the resultsof your efforts: The lowest unemployment rate in over five years. Arebounding housing market. A manufacturing sector that's adding jobs forthe first time since the 1990s. More oil produced at home than we buyfrom the rest of the world—the first time that's happened in nearlytwenty years. Our deficits—cut by more than half. And for the first timein over a decade, business leaders around the world have declared thatChina is no longer the world's number one place to invest; America is.”Within this paragraph, there are six claims that are verifiable throughthe use of an authoritative source and annotated as such within thetraining corpus of text 304. Likewise, the initial statement of “Hereare the results of your efforts:” is a declaration and is annotated asnot verifiable. In certain embodiments, the training corpus of text 304is combined with a set of unlabeled arguments.

The training corpus of text 304 is then segmented into statements, suchas independent clauses, by a segmenter module 304. In variousembodiments, the segmenter module 304 is implemented to perform varioussegmentation operations, such as sentence segmentation, textsegmentation, or a combination thereof. As used herein, sentencesegmentation broadly refers to the process of determining textprocessing units consisting of one or more words and likewiseidentifying sentence boundaries between words in different sentences.Those of skill in the art will be aware that most written languages havepunctuation marks which occur at sentence boundaries. Accordingly,sentence segmentation is frequently referred to as sentence boundarydetection, sentence boundary disambiguation, or sentence boundaryrecognition. All these terms refer to the same task, which isdetermining how a corpus of text should be divided into sentences forfurther processing.

As likewise used herein, text segmentation broadly refers to the task ofdividing a corpus of text into linguistically-meaningful units. As usedherein, these linguistic units refer to the lowest level charactersrepresenting individual graphemes in a language's written system, wordsconsisting of one or more characters, and sentences consisting of one ormore words. Skilled practitioners of the art will be aware that it isdifficult to successfully perform sentence and word segmentationindependent from one another.

To continue the preceding example, the six statements within thePresident's 2014 State of the Union Speech that have been annotated asverifiable are segmented to generate the following verifiablestatements:

-   -   The unemployment rate is the lowest in over five years    -   The housing market rebounding    -   The manufacturing sector is adding jobs for the first time since        the 1990s    -   More oil is being produced In the US than is bought from the        rest of the world for the first time in nearly twenty years    -   Deficits have been cut by more than half.    -   Business leaders around the world have declared that America,        not China, is the world's number one place to invest for the        first time in a decade

Likewise, the initial statement of “Here are the results of yourefforts:” is segmented as a declaration that is not verifiable.

Features 312 are then extracted from the resulting segmented statements308 by a feature extractor module 308. As used herein, featureextraction broadly refers to the process of simplifying the amount ofresources required to accurately describe a large set of data. Incertain embodiments, the feature extractor module 308 performs featureextraction operations by utilizing named entities, which refer to aplace holder for a particular feature or piece of information. Examplesof named entities include service names, product names, country names,proper names, and so forth. In certain embodiments, each named entity isassociated with a list or grammar of possible phrases. For example, anamed entity associated with a country may be associated with a list ofnames such as “US,” “America,” “China,” and so forth.

In various embodiments, In various embodiments, the extracted features312 may include semantic features. In certain of these embodiments, thesemantic features may be represented in a notational form to express theexistence or non-existence of pre-established semantic properties. Forexample, the word “man” may be represented as [+HUMAN], [+MALE],[+ADULT], and the word “woman” may be represented as [−HUMAN], [−MALE],[+−ADULT]. Likewise, the word boy may be represented as [+HUMAN],[+MALE], [−ADULT], and the word “girl” may be represented as [+HUMAN],[−MALE], [−ADULT]. The method by which the method by which the extractedfeatures 312 are represented and notated is a matter of design choice.In various embodiments, the extracted features may correspond tosentiment, verbs, verb tense, nouns, proper nouns, magnitude, velocity,importance, quantified items, quantitative comparison operators, orreference.

The extracted features 312 and the annotated statements 314 within thetraining corpus of text 304 are then processed by a machine learningalgorithm 316 to generate a verifiable statement classification model318. In certain embodiments, the extracted features 312 and theannotated statements 314 within the training corpus of text 304 areprocessed by a machine learning algorithm 316 to update an existingverifiable statement classification model 318. In various embodiments,named entity phrases within the training corpus of text 304 may be usedto construct grammars, regular expressions, or statistical models, suchas the verifiable statement model 318.

In various embodiments, verifiable statement identification operationsare initiated by first receiving an input corpus of text 322 containingstatements to be classified as verifiable or not verifiable. Forexample, the input corpus of text 322 may include a paragraph of text,such as, “I am a board-certified cardiologist, and I received my medicaldegree from Johns Hopkins University twenty years ago. I find thepractice of medicine rewarding on a personal level and I intend tocontinue my practice for another ten years.” Once the input corpus oftext 322 has been received, it is then segmented into statements 308 bythe segmenter module 306, as described in greater detail herein. Tocontinue the example, the resulting segmented statements may include “Iam a board-certified cardiologist,” “I received my medical degree fromJohns Hopkins University twenty years ago,” “I find the practice ofmedicine rewarding on a personal level,” and “I intend to continue mypractice for another ten years.”

In turn, the feature extractor module 310 processes the segmentedstatements 308, as described in greater detail herein, to classify themas verifiable or not verifiable by referencing them to the verifiablestatement classification model 318. Continuing the example, the firsttwo segmented statements are classified as being verifiable, as theirveracity can be verified by an authoritative source, such as a medicalboard that issues licenses and the university that awarded the degree.However, the second two segmented statements are not verifiable, as theyare personal assertions that cannot be verified by an authoritativesource.

In various embodiments, the feature extractor module 310 may use theoutputs of a sentiment analysis module 318 to classify the segmentedstatements 308 as verifiable or not verifiable. In certain embodiments,the feature extractor module 310 may use the outputs of a languageknowledge module 320 to classify the segmented statements 308 asverifiable or not verifiable. In these embodiments, the languageknowledge module 320 may provide information associated the segmentedstatements 308, such as verb tense (e.g., past, present, future), thenumber of proper nouns, or various metrics associated with the amount ofpublicly available information about each proper noun (e.g., the numberof times that a proper noun occurs in Wikipedia).

In various embodiments, the language knowledge module 320 may likewiseprovide the feature extractor module 310 a list of known verbs, oradjectives that imply magnitude or velocity, such as “is increasing,”“highest,” and so forth. In certain embodiments, the language knowledgemodule 320 may likewise provide information to the feature extractormodule 310 related to quantified items, such as “500,000 jobs,” orpredetermined semantic frames such as “manufacturers add jobs.” Invarious embodiments, the relative importance of particular semanticframes may be determined based upon a set of training data, such asgathered from FactCheck.org or from fact-based sources such asnewspapers and government reports. In certain embodiments, the languageknowledge module 320 may likewise provide the feature extractor module310 information related to quantitative comparison operators, such as“more than last year.” Skilled practitioners of the art will recognizethat many such embodiments are possible and the foregoing is notintended to limit the spirit, scope or intent of the invention.

In various embodiments, the segmented statements 308 are processed by aNatural Language Processing (NLP) system, familiar to those of skill inthe art, to perform part-of-speech tagging and syntacticpredicate-argument analysis. This analysis connects predicates and theirarguments into semantic frames. As a result of this analysis process, alist of classified statements 324 to be verified is produced, along withassociated NLP analysis-derived information, which can then be providedto downstream processes, such as a fact verification system 326, forverification. The design and operation of such downstream processes orfactual verification systems are a matter of design choice.

FIG. 4 is a generalized flowchart of operations performed in accordancewith an embodiment of the invention to generate a verifiable statementclassification model. In this embodiment, verifiable statementclassification model generation operations are begun in step 402,followed by the receipt of a candidate training corpus of textcontaining statements in step 404. A training corpus of text is thengenerated in step 406 by manually annotating statements within thecandidate training corpus of text 406 as verifiable or not verifiable.The resulting training corpus of text is segmented in step 408 intostatements, such as independent clauses. In various embodiments, thetraining corpus of text is segmented by a segmenter module, such as thesegmenter module 304 shown in FIG. 3.

Features are then extracted from the segmented statements in step 410.In various embodiments, the features are extracted from the segmentedstatements by a feature extractor module, such as the feature extractormodule 308 shown in FIG. 3. The extracted features and the annotatedstatements within the training corpus of text are then provided in step412 to a machine learning algorithm for processing, followed by adetermination being made in step 414 whether a verifiable statementclassification model currently exists. If not, the extracted featuresand the annotated statements within the training corpus of text areprocessed in step 416 by the machine learning algorithm to generate anew verifiable statement classification model. Otherwise, the extractedfeatures and the annotated statements within the training corpus of textare processed in step 418 by the machine learning algorithm to update atarget existing verifiable statement classification model.

Once the new verifiable statement classification model is generated instep 416, or a target existing verifiable statement classification modelis updated in step 418, a determination is made in step 420 whether toend verifiable statement classification model generation operations. Ifnot, then the process is continued, proceeding with step 404. Otherwise,verifiable statement classification model generation operations areended in step 422.

FIG. 5 is a generalized flowchart of the performance of operationsperformed in accordance with an embodiment of the invention to identifyverifiable statements in a corpus of text. In this embodiment,verifiable statement identification operations are begun in step 502,followed by the receipt of an input corpus of content containingstatements in step 504. A determination is then made in step 506 whetherthe input corpus of content is in the audio or text form. For example,the input corpus of content may be a running audio of a speech inprogress, the transcript of the same speech, or a document from someother source to be verified, such as a web page.

If it is determined in step 506 that the input corpus of content is inaudio form, then a determination is made in step 508 whether the audiocontent is recorded or live. If it is determined in step 508 that theaudio content is live, then it is converted into text in step 510.However, if it determined then the recorded audio content is convertedto an input corpus of text in step 510. Otherwise, the recorded audiocontent is converted into an input corpus of text in step 512.

Once audio content has been converted into an input corpus of text instep 510 or 512, or if it was determined in step 506 that the inputcorpus of content was already in the form of text, the input corpus oftext is then segmented into statements in step 514. The resultingstatements are then processed in step 516 to classify them as verifiableor not verifiable by referencing them to a verifiable statementclassification model, described in greater detail herein. Adetermination is then made in step 518 whether to end verifiablestatement identification operations. If not, then the process iscontinued, proceeding with step 504. Otherwise, verifiable statementidentification operations are ended in step 520.

Although the present invention has been described in detail, it shouldbe understood that various changes, substitutions and alterations can bemade hereto without departing from the spirit and scope of the inventionas defined by the appended claims.

1-6. (canceled)
 7. A system comprising: a processor; a data bus coupledto the processor; and a computer-usable medium embodying computerprogram code, the computer-usable medium being coupled to the data bus,the computer program code used for identifying verifiable statements andcomprising instructions executable by the processor and configured for:receiving a text containing a plurality of statements, the text receivedby a system configured to parse text input; processing the text to parsethe plurality of statements into segmented statements; and processingthe segmented statements to identify individual segmented statementsthat are verifiable, the identification performed by the system.
 8. Thesystem of claim 7, further comprising: processing the segmentedstatements to extract features associated with the statements, thefeature extraction performed by the system.
 9. The system of claim 8,wherein individual extracted features correspond to at least one memberof the set of: sentiment; verbs; verb tense; nouns; proper nouns;magnitude; velocity; importance; quantified items; quantitativecomparison operators; and reference.
 10. The system of claim 8, furthercomprising: receiving a training text comprising annotated verifiablestatements; and processing the extracted features and the annotatedverifiable statements to generate a verifiable statement classificationmodel, the generation performed by the system.
 11. The system of claim10, wherein: a machine learning algorithm is used by the system toperform the generation of the verifiable statement classification model.12. The system of claim 11, wherein: the system performs theidentification by referencing the verifiable statement classificationmodel.
 13. A non-transitory, computer-readable storage medium embodyingcomputer program code, the computer program code comprising computerexecutable instructions configured for: receiving a text containing aplurality of statements, the text received by a system configured toparse text input; processing the text to parse the plurality ofstatements into segmented statements; and processing the segmentedstatements to identify individual segmented statements that areverifiable, the identification performed by the system.
 14. Thenon-transitory, computer-readable storage medium of claim 13, furthercomprising: processing the segmented statements to extract featuresassociated with the statements, the feature extraction performed by thesystem.
 15. The non-transitory, computer-readable storage medium ofclaim 14, wherein individual extracted features correspond to at leastone member of the set of: sentiment; verbs; verb tense; nouns; propernouns; magnitude; velocity; importance; quantified items; quantitativecomparison operators; and reference.
 16. The non-transitory,computer-readable storage medium of claim 14, further comprising:receiving a training text comprising annotated verifiable statements;and processing the extracted features and the annotated verifiablestatements to generate a verifiable statement classification model, thegeneration performed by the system.
 17. The non-transitory,computer-readable storage medium of claim 16, wherein: a machinelearning algorithm is used by the system to perform the generation ofthe verifiable statement classification model.
 18. The non-transitory,computer-readable storage medium of claim 17, wherein: the systemperforms the identification by referencing the verifiable statementclassification model.
 19. The non-transitory, computer-readable storagemedium of claim 13, wherein the computer executable instructions aredeployable to a client system from a server system at a remote location.20. The non-transitory, computer-readable storage medium of claim 13,wherein the computer executable instructions are provided by a serviceprovider to a user on an on-demand basis.